Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to
automate the information creation process while fulfilling the personalized
requirements of users. However, due to the instability of AIGC models, e.g.,
the stochastic nature of diffusion models, the quality and accuracy of the
generated content can vary significantly. In wireless edge networks, the
transmission of incorrectly generated content may unnecessarily consume network
resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is
required to enable users to connect to the most suited ASP, improving the
users' satisfaction and quality of generated content. In this article, we first
review the AIGC techniques and their applications in wireless networks. We then
present the AIGC-as-a-service (AaaS) concept and discuss the challenges in
deploying AaaS at the edge networks. Yet, it is essential to have performance
metrics to evaluate the accuracy of AIGC services. Thus, we introduce several
image-based perceived quality evaluation metrics. Then, we propose a general
and effective model to illustrate the relationship between computational
resources and user-perceived quality evaluation metrics. To achieve efficient
AaaS and maximize the quality of generated content in wireless edge networks,
we propose a deep reinforcement learning-enabled algorithm for optimal ASP
selection. Simulation results show that the proposed algorithm can provide a
higher quality of generated content to users and achieve fewer crashed tasks by
comparing with four benchmarks, i.e., overloading-avoidance, random,
round-robin policies, and the upper-bound schemes.

本文介绍了人工智能生成内容（AIGC）的技术及其在无线边缘网络中的应用，提出了 AIGC 作为服务（AaaS）的概念，并讨论了在边缘网络上部署 AaaS 的挑战。同时，我们还引入了几个基于图像的感知质量评估指标，并提出了一种通用有效的模型来说明计算资源与用户感知质量评估指标之间的关系。为了实现高效的 AaaS 和最大化在无线边缘网络中生成内容的质量，我们提出了一种深度强化学习启发的算法来选择最佳 ASP。仿真结果表明，相对于四个基准策略，即避免过载，随机，轮流以及上限方案，我们提出的算法可以为用户提供更高质量的生成内容并实现更少的崩溃任务。